A Novel K-Harmonic Means Clustering Based on Multiple Initial Centers

Abstract:

In the initialization of the traditional k-harmonic means clustering, the initial centers are generated randomly and its number is equal to the number of clusters. Although the k-harmonic means clustering is insensitive to the initial centers, this initialization method cannot improve clustering performance. In this paper, a novel k-harmonic means clustering based on multiple initial centers is proposed. The number of the initial centers is more than the number of clusters in this new method. The new method with multiple initial centers can divide the whole data set into multiple groups and combine these groups into the final solution. Experiments show that the presented algorithm can increase the better clustering accuracies than the traditional k-means and k-harmonic methods.

• Periodical: Applied Mechanics and Materials (Volumes 321-324)
• Pages: 1947-1950
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.321-324.1947
• Online since: June 2013
• Authors: Lei Gu, Xian Ling Lu
• Keywords: Clustering, Initialization, K-Harmonic Means, K-Mean

References

[1] A.K. Jain, M.N. Murty, P.J. Flyn, Data clustering: a review. ACM Computing Surveys, Vol.31, No.3 (1999), pp.256-323.

[2] R. Xu, D. Wunsch, Survey of clustering algorithms. IEEE Transactions on Neural Net-works, Vol.16, No.3 (2005), pp.645-678.

[3] J.T. Tou, R.C. Gonzalez, Pattern recognition principles. Addison-Wesley, London (1974).

[4] J.C. Bezdek, Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York (1981).

[5] R. Krishnapuram, A. Joshi, O. Nasraoui, L. Yi, Low complexity fuzzy relational clustering algorithms for web mining. IEEE Transactions on Fuzzy Systems, Vol.9, No.4, (2001), pp.595-607.

DOI: 10.1109/91.940971

[6] L. Kaufman, P.J. Rousseeuw, Finding groups in data: an introduction to cluster analysis. John Wiley & Sons, New York (1990).

[7] T.M. Martinetz, S.G. Berkovich, K.J. Schulten, Neural-gas network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks, Vol.4, No.4, (1993), pp.558-569.

DOI: 10.1109/72.238311

[8] B. Zhang, M. Hus, U. Dayal, K-harmonic means－a data clustering algorithm. Technical Report HPL-1999-124, Hewlett-Packard Laboratories, (1999).

[9] B. Zhang, M. Hus, U. Dayal, K-harmonic means. In Proceedings of International Workshop on Temporal, Spatial and Spatio-temporal data mining, Lyon, France, (2000).

[10] F.Q. Yang, T.L. Sun, C.H. Zhang, An efficient hydrid data clustering method based on k- harmonic means and particle swarm optimization. Expert Systems with Applications, Vol.36, No.6, (2009), pp.9847-9852.

DOI: 10.1016/j.eswa.2009.02.003

[11] C. Hammerly, C. Elkan, Alternative to the k-means algorithm that find better clusterings. In Proceedings of the 11th International Conference on Information and Knowledge Management, (2002), pp.600-607.

DOI: 10.1145/584792.584890

[12] C. Ormella, M. Anastasios, S. Sandhya, K. Don, L. Sijia, K.M. Philip, E. Radek, DifFUZZY: a fuzzy clustering algorithm for complex datasets. International Journal of Computational Intel- ligence in Bioinformatics and Systems Biology, Vol.1, No.4, (2010), pp.402-417.

[13] UCI Machine Learning Repository, http://www.ics.uci.edu/~mlearn/MLSummary.html.